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   "source": [
    "# Fictitious Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data again \n",
    "\n",
    "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n",
    "All the credits to this exercise belongs to him.  \n",
    "\n",
    "In order to understand about it go [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
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   "source": [
    "### Step 2. Create the 3 DataFrames based on the following raw data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign each to a variable called data1, data2, data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Join the two dataframes along rows and assign all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Join the two dataframes along columns and assing to all_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "cell_type": "markdown",
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   "source": [
    "### Step 6. Print data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Merge all_data and data3 along the subject_id value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Merge all values in data1 and data2, with matching records from both sides where available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
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